Low-intensity shockwave therapy improves baseline erectile function: a randomized sham-controlled crossover trial

Background

Low-intensity shockwave therapy for erectile dysfunction is emerging as a promising treatment option.

Aim

This randomized sham-controlled crossover trial assessed the efficacy of low-intensity shockwave therapy in the treatment of erectile dysfunction.

Methods

Thirty-three participants with organic erectile dysfunction were enrolled and randomized to shockwave therapy (n = 17) or sham (n = 16). The sham group was allowed to cross over to receive shockwave therapy after 1 month.

Outcomes

Primary outcomes were the changes in Sexual Health Inventory for Men (SHIM) score and Erection Hardness Score at 1 month following shockwave therapy vs sham, and secondary outcomes were erectile function measurements at 1, 3, and 6 months following shockwave therapy.

Results

At 1 month, mean SHIM scores were significantly increased in the shockwave therapy arm as compared with the sham arm (+3.0 vs -0.7, P = .024). Participants at 6 months posttreatment (n = 33) showed a mean increase of 5.5 points vs baseline (P < .001), with 20 (54.6%) having an increase ≥5. Of the 25 men with an initial Erection Hardness Score <3, 68% improved to a score ≥3 at 6 months. When compared with baseline, the entire cohort demonstrated significant increases in erectile function outcomes at 1, 3, and 6 months after treatment.

Clinical Implications

In this randomized sham-controlled crossover trial, we showed that 54.6% of participants with organic erectile dysfunction met the minimal clinically important difference in SHIM scores after treatment with low-intensity shockwave therapy.

Strengths and Limitations

Strengths of this study include a sham-controlled group that crossed over to treatment. Limitations include a modest sample size at a single institution.

Conclusions

Low-intensity shockwave therapy improves erectile function in men with erectile dysfunction as compared with sham treatment, which persists even 6 months after treatment.

Clinical Trial Registration

ClinicalTrials.gov NCT04434352.

The impact of cognitive impairment in urologic implants: a narrative review

Background and Objective

With the general population aging and thus more patients developing bothersome erectile dysfunction, stress urinary incontinence and overactive bladder, there will likely be a higher demand for three common interactive implants in urology, the penile prosthesis, artificial urinary sphincter (AUS) and sacral neuromodulation (SNM). Further, the prevalence of mild and major neurocognitive disorders (also known as mild cognitive impairment and dementia, respectively) is expected to increase. While the aforementioned urologic implants have excellent short and long term outcomes, there are also known device issues such as malfunction or misuse that may require surgical removal and/or revision. The objective of this narrative review is to describe the association of cognitive impairment and urologic implants.

Methods

We performed a search on PubMed between the years 1975-2023 for English language articles that reported on any type or severity of cognitive impairment and its association with penile prosthesis, AUS and/or SNM. While peer-reviewed published manuscripts were prioritized, abstracts that fit our search criteria were also included.

Key Content and Findings

Data assessing outcomes of patients with cognitive impairment who undergo placement of a urologic implant are limited. There is an association between AUS failure or misuse with cognitive impairment. SNM is efficacious in this population in the short term. In patients who develop dementia, an inflatable penile prosthesis can be deflated via in-office needle puncture and an AUS can be deactivated. The Memory Alteration Test, Quick Screen for Mild Cognitive Impairment and the Saint Louis University Mental Status Examination are relatively quick screening tests with good sensitivity and specificity for mild cognitive impairment.

Conclusions

While data on the association between urologic implants and cognitive impairment are sparse, there are tools that urologists can use to screen patients for cognitive impairment. With screening, urologists can provide appropriate preoperative counseling (including recommending against implantation) and can provide closer postoperative monitoring. Further study is required to assess which patients should be excluded from device implantation and how to properly assess for cognitive impairment in a manner that is both beneficial for the patient and convenient and efficient for a urologist.

Longitudinal urgency outcomes following robotic-assisted laparoscopic prostatectomy

PURPOSE

Stress urinary incontinence (SUI) is a well-known adverse outcome following robotic-assisted laparoscopic prostatectomy (RALP). Although postoperative SUI has been extensively studied, little focus has been placed on understanding the natural history and impact of urgency symptoms following RALP. The UVA prostatectomy functional outcomes program (PFOP) was developed to comprehensively assess and optimize continence outcomes following RALP. The present study focuses on assessing urgency outcomes in this cohort.

METHODS

PFOP patients with a minimum of 6-months follow up following RALP were included. The PFOP includes prospectively assessed incontinence and quality of life outcomes utilizing ICIQ-MLUTS, Urgency Perception Score (UPS), and IIQ-7 questionnaires. The primary study outcome was urgency urinary incontinence (UUI) as determined by ICIQ-MLUTS UUI domain. Secondary outcomes included urgency (UPS score) and quality of life (IIQ-7).

RESULTS

Forty patients were included with median age 63.5 years. Fourteen (35%) patients reported UUI at baseline. UUI and QOL scores worsened compared to baseline at all time-points. Urgency worsened at 3-weeks and 3-months but returned to baseline by 6-months. Notably, 63% of patients without baseline UUI reported de-novo UUI at 6 months. Although QOL was lower in patients with versus without UUI (IIQ-7 score 3.0 vs 0.0, p = 0.009), severity of UUI was not associated with QOL when controlling for SUI severity.

CONCLUSION

Our data demonstrate significantly worsened UUI from baseline and a large incidence of de-novo UUI following RALP. Further study is needed to inform how urgency and UUI and its treatment affect health-related quality of life following RALP.

A rare presentation of unilateral periureteral renal lymphangiomatosis

Renal lymphangiomatosis is a rare developmental malformation of the perirenal lymphatic system. We report a unique case with unilateral massive periureteral involvement in addition to intrarenal and peripelvic lymphangiomatosis. Although this is a rare entity, it should be considered in patients with peripelvic or periureteric cystic lesions as it may affect appropriate management and follow-up. This case report reviews the imaging features of this entity and a comprehensive literature review and discussion about the entity will be provided.

A comparison of machine learning classifiers for pediatric epilepsy using resting-state functional MRI latency data

Epilepsy affects 1 in 150 children under the age of 10 and is the most common chronic pediatric neurological condition; poor seizure control can irreversibly disrupt normal brain development. The present study compared the ability of different machine learning algorithms trained with resting-state functional MRI (rfMRI) latency data to detect epilepsy. Preoperative rfMRI and anatomical MRI scans were obtained for 63 patients with epilepsy and 259 healthy controls. The normal distribution of latency z-scores from the epilepsy and healthy control cohorts were analyzed for overlap in 36 seed regions. In these seed regions, overlap between the study cohorts ranged from 0.44-0.58. Machine learning features were extracted from latency z-score maps using principal component analysis. Extreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), and Random Forest algorithms were trained with these features. Area under the receiver operating characteristics curve (AUC), accuracy, sensitivity, specificity and F1-scores were used to evaluate model performance. The XGBoost model outperformed all other models with a test AUC of 0.79, accuracy of 74%, specificity of 73%, and a sensitivity of 77%. The Random Forest model performed comparably to XGBoost across multiple metrics, but it had a test sensitivity of 31%. The SVM model did not perform >70% in any of the test metrics. The XGBoost model had the highest sensitivity and accuracy for the detection of epilepsy. Development of machine learning algorithms trained with rfMRI latency data could provide an adjunctive method for the diagnosis and evaluation of epilepsy with the goal of enabling timely and appropriate care for patients.